Overview

Dataset statistics

Number of variables5
Number of observations1323
Missing cells5
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.8 KiB
Average record size in memory40.1 B

Variable types

Text5

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20461/S/1/datasetView.do

Reproduction

Analysis started2024-03-23 04:15:37.843472
Analysis finished2024-03-23 04:15:38.650386
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

Distinct1322
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Memory size10.5 KiB
2024-03-23T13:15:38.975592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9969743
Min length6

Characters and Unicode

Total characters13216
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1322 ?
Unique (%)100.0%

Sample

1st row서울시기준일
2nd row2020-01-19
3rd row2020-01-20
4th row2020-01-21
5th row2020-01-22
ValueCountFrequency (%)
2020-01-19 1
 
0.1%
2022-06-21 1
 
0.1%
2022-06-19 1
 
0.1%
2022-06-18 1
 
0.1%
2022-06-17 1
 
0.1%
2022-06-16 1
 
0.1%
2022-06-15 1
 
0.1%
2022-06-14 1
 
0.1%
2022-06-13 1
 
0.1%
2022-06-12 1
 
0.1%
Other values (1312) 1312
99.2%
2024-03-23T13:15:39.652511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3780
28.6%
0 3321
25.1%
- 2642
20.0%
1 1424
 
10.8%
3 563
 
4.3%
8 254
 
1.9%
7 254
 
1.9%
5 254
 
1.9%
6 250
 
1.9%
4 250
 
1.9%
Other values (7) 224
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10568
80.0%
Dash Punctuation 2642
 
20.0%
Other Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3780
35.8%
0 3321
31.4%
1 1424
 
13.5%
3 563
 
5.3%
8 254
 
2.4%
7 254
 
2.4%
5 254
 
2.4%
6 250
 
2.4%
4 250
 
2.4%
9 218
 
2.1%
Other Letter
ValueCountFrequency (%)
서 1
16.7%
울 1
16.7%
시 1
16.7%
기 1
16.7%
준 1
16.7%
일 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 2642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13210
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3780
28.6%
0 3321
25.1%
- 2642
20.0%
1 1424
 
10.8%
3 563
 
4.3%
8 254
 
1.9%
7 254
 
1.9%
5 254
 
1.9%
6 250
 
1.9%
4 250
 
1.9%
Hangul
ValueCountFrequency (%)
서 1
16.7%
울 1
16.7%
시 1
16.7%
기 1
16.7%
준 1
16.7%
일 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13210
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3780
28.6%
0 3321
25.1%
- 2642
20.0%
1 1424
 
10.8%
3 563
 
4.3%
8 254
 
1.9%
7 254
 
1.9%
5 254
 
1.9%
6 250
 
1.9%
4 250
 
1.9%
Hangul
ValueCountFrequency (%)
서 1
16.7%
울 1
16.7%
시 1
16.7%
기 1
16.7%
준 1
16.7%
일 1
16.7%

- ′23.8.31일 0시 기준, 질병관리청 질병보건통합관리시스템에 신고된 코로나19 확진환자
Text

Distinct959
Distinct (%)72.5%
Missing1
Missing (%)0.1%
Memory size10.5 KiB
2024-03-23T13:15:40.098558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length3.774584
Min length1

Characters and Unicode

Total characters4990
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique818 ?
Unique (%)61.9%

Sample

1st row서울시 일자별 확진자
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 38
 
2.9%
2 15
 
1.1%
12 14
 
1.1%
9 12
 
0.9%
6 12
 
0.9%
7 11
 
0.8%
5 10
 
0.8%
3 9
 
0.7%
11 9
 
0.7%
13 8
 
0.6%
Other values (951) 1186
89.6%
2024-03-23T13:15:40.649640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 769
15.4%
, 629
12.6%
2 570
11.4%
5 440
8.8%
4 417
8.4%
3 411
8.2%
7 367
7.4%
0 352
7.1%
9 348
7.0%
6 346
6.9%
Other values (10) 341
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4350
87.2%
Other Punctuation 629
 
12.6%
Other Letter 9
 
0.2%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 769
17.7%
2 570
13.1%
5 440
10.1%
4 417
9.6%
3 411
9.4%
7 367
8.4%
0 352
8.1%
9 348
8.0%
6 346
8.0%
8 330
7.6%
Other Letter
ValueCountFrequency (%)
자 2
22.2%
서 1
11.1%
울 1
11.1%
시 1
11.1%
일 1
11.1%
별 1
11.1%
확 1
11.1%
진 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 629
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4981
99.8%
Hangul 9
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 769
15.4%
, 629
12.6%
2 570
11.4%
5 440
8.8%
4 417
8.4%
3 411
8.3%
7 367
7.4%
0 352
7.1%
9 348
7.0%
6 346
6.9%
Other values (2) 332
6.7%
Hangul
ValueCountFrequency (%)
자 2
22.2%
서 1
11.1%
울 1
11.1%
시 1
11.1%
일 1
11.1%
별 1
11.1%
확 1
11.1%
진 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4981
99.8%
Hangul 9
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 769
15.4%
, 629
12.6%
2 570
11.4%
5 440
8.8%
4 417
8.4%
3 411
8.3%
7 367
7.4%
0 352
7.1%
9 348
7.0%
6 346
6.9%
Other values (2) 332
6.7%
Hangul
ValueCountFrequency (%)
자 2
22.2%
서 1
11.1%
울 1
11.1%
시 1
11.1%
일 1
11.1%
별 1
11.1%
확 1
11.1%
진 1
11.1%

Unnamed: 2
Text

Distinct1285
Distinct (%)97.2%
Missing1
Missing (%)0.1%
Memory size10.5 KiB
2024-03-23T13:15:40.982464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.8517398
Min length1

Characters and Unicode

Total characters9058
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1272 ?
Unique (%)96.2%

Sample

1st row서울시 누적 확진자
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
12 10
 
0.8%
637 6
 
0.5%
1 6
 
0.5%
0 5
 
0.4%
633 3
 
0.2%
628 3
 
0.2%
14 3
 
0.2%
629 3
 
0.2%
8 3
 
0.2%
624 2
 
0.2%
Other values (1277) 1280
96.7%
2024-03-23T13:15:41.490738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 1724
19.0%
6 836
9.2%
3 822
9.1%
5 813
9.0%
1 813
9.0%
2 802
8.9%
4 736
8.1%
0 651
 
7.2%
8 627
 
6.9%
7 619
 
6.8%
Other values (10) 615
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7324
80.9%
Other Punctuation 1724
 
19.0%
Other Letter 8
 
0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 836
11.4%
3 822
11.2%
5 813
11.1%
1 813
11.1%
2 802
11.0%
4 736
10.0%
0 651
8.9%
8 627
8.6%
7 619
8.5%
9 605
8.3%
Other Letter
ValueCountFrequency (%)
서 1
12.5%
울 1
12.5%
시 1
12.5%
누 1
12.5%
적 1
12.5%
확 1
12.5%
진 1
12.5%
자 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 1724
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9050
99.9%
Hangul 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
, 1724
19.0%
6 836
9.2%
3 822
9.1%
5 813
9.0%
1 813
9.0%
2 802
8.9%
4 736
8.1%
0 651
 
7.2%
8 627
 
6.9%
7 619
 
6.8%
Other values (2) 607
 
6.7%
Hangul
ValueCountFrequency (%)
서 1
12.5%
울 1
12.5%
시 1
12.5%
누 1
12.5%
적 1
12.5%
확 1
12.5%
진 1
12.5%
자 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9050
99.9%
Hangul 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 1724
19.0%
6 836
9.2%
3 822
9.1%
5 813
9.0%
1 813
9.0%
2 802
8.9%
4 736
8.1%
0 651
 
7.2%
8 627
 
6.9%
7 619
 
6.8%
Other values (2) 607
 
6.7%
Hangul
ValueCountFrequency (%)
서 1
12.5%
울 1
12.5%
시 1
12.5%
누 1
12.5%
적 1
12.5%
확 1
12.5%
진 1
12.5%
자 1
12.5%

Unnamed: 3
Text

Distinct55
Distinct (%)4.2%
Missing1
Missing (%)0.1%
Memory size10.5 KiB
2024-03-23T13:15:41.741455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.1520424
Min length1

Characters and Unicode

Total characters1523
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.0%

Sample

1st row서울시 일자별 사망자
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 371
28.0%
1 231
17.4%
2 164
12.4%
3 112
 
8.5%
4 78
 
5.9%
5 58
 
4.4%
6 36
 
2.7%
8 32
 
2.4%
7 30
 
2.3%
11 21
 
1.6%
Other values (47) 191
14.4%
2024-03-23T13:15:42.074474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 396
26.0%
1 372
24.4%
2 229
15.0%
3 148
 
9.7%
4 118
 
7.7%
5 70
 
4.6%
6 65
 
4.3%
8 43
 
2.8%
7 43
 
2.8%
9 28
 
1.8%
Other values (9) 11
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1512
99.3%
Other Letter 9
 
0.6%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 396
26.2%
1 372
24.6%
2 229
15.1%
3 148
 
9.8%
4 118
 
7.8%
5 70
 
4.6%
6 65
 
4.3%
8 43
 
2.8%
7 43
 
2.8%
9 28
 
1.9%
Other Letter
ValueCountFrequency (%)
자 2
22.2%
서 1
11.1%
울 1
11.1%
시 1
11.1%
일 1
11.1%
별 1
11.1%
사 1
11.1%
망 1
11.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1514
99.4%
Hangul 9
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 396
26.2%
1 372
24.6%
2 229
15.1%
3 148
 
9.8%
4 118
 
7.8%
5 70
 
4.6%
6 65
 
4.3%
8 43
 
2.8%
7 43
 
2.8%
9 28
 
1.8%
Hangul
ValueCountFrequency (%)
자 2
22.2%
서 1
11.1%
울 1
11.1%
시 1
11.1%
일 1
11.1%
별 1
11.1%
사 1
11.1%
망 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1514
99.4%
Hangul 9
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 396
26.2%
1 372
24.6%
2 229
15.1%
3 148
 
9.8%
4 118
 
7.8%
5 70
 
4.6%
6 65
 
4.3%
8 43
 
2.8%
7 43
 
2.8%
9 28
 
1.8%
Hangul
ValueCountFrequency (%)
자 2
22.2%
서 1
11.1%
울 1
11.1%
시 1
11.1%
일 1
11.1%
별 1
11.1%
사 1
11.1%
망 1
11.1%

Unnamed: 4
Text

Distinct952
Distinct (%)72.0%
Missing1
Missing (%)0.1%
Memory size10.5 KiB
2024-03-23T13:15:42.454156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length3.5945537
Min length1

Characters and Unicode

Total characters4752
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique831 ?
Unique (%)62.9%

Sample

1st row서울시 누적 사망자
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 80
 
6.0%
1 35
 
2.6%
3 34
 
2.6%
77 16
 
1.2%
10 14
 
1.1%
12 11
 
0.8%
8 10
 
0.8%
6 9
 
0.7%
5 7
 
0.5%
6,476 6
 
0.5%
Other values (944) 1102
83.2%
2024-03-23T13:15:43.044885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 644
13.6%
5 546
11.5%
6 542
11.4%
4 526
11.1%
1 432
9.1%
3 406
8.5%
2 401
8.4%
0 340
7.2%
8 334
7.0%
7 315
6.6%
Other values (10) 266
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4098
86.2%
Other Punctuation 644
 
13.6%
Other Letter 8
 
0.2%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 546
13.3%
6 542
13.2%
4 526
12.8%
1 432
10.5%
3 406
9.9%
2 401
9.8%
0 340
8.3%
8 334
8.2%
7 315
7.7%
9 256
6.2%
Other Letter
ValueCountFrequency (%)
서 1
12.5%
울 1
12.5%
시 1
12.5%
누 1
12.5%
적 1
12.5%
사 1
12.5%
망 1
12.5%
자 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 644
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4744
99.8%
Hangul 8
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
, 644
13.6%
5 546
11.5%
6 542
11.4%
4 526
11.1%
1 432
9.1%
3 406
8.6%
2 401
8.5%
0 340
7.2%
8 334
7.0%
7 315
6.6%
Other values (2) 258
5.4%
Hangul
ValueCountFrequency (%)
서 1
12.5%
울 1
12.5%
시 1
12.5%
누 1
12.5%
적 1
12.5%
사 1
12.5%
망 1
12.5%
자 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4744
99.8%
Hangul 8
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 644
13.6%
5 546
11.5%
6 542
11.4%
4 526
11.1%
1 432
9.1%
3 406
8.6%
2 401
8.5%
0 340
7.2%
8 334
7.0%
7 315
6.6%
Other values (2) 258
5.4%
Hangul
ValueCountFrequency (%)
서 1
12.5%
울 1
12.5%
시 1
12.5%
누 1
12.5%
적 1
12.5%
사 1
12.5%
망 1
12.5%
자 1
12.5%

Missing values

2024-03-23T13:15:38.199863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T13:15:38.370291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-23T13:15:38.534575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0- ′23.8.31일 0시 기준, 질병관리청 질병보건통합관리시스템에 신고된 코로나19 확진환자Unnamed: 2Unnamed: 3Unnamed: 4
0<NA><NA><NA><NA><NA>
1서울시기준일서울시 일자별 확진자서울시 누적 확진자서울시 일자별 사망자서울시 누적 사망자
22020-01-190000
32020-01-200000
42020-01-210000
52020-01-220000
62020-01-230000
72020-01-241100
82020-01-250100
92020-01-260100
Unnamed: 0- ′23.8.31일 0시 기준, 질병관리청 질병보건통합관리시스템에 신고된 코로나19 확진환자Unnamed: 2Unnamed: 3Unnamed: 4
13132023-08-229,6726,689,17636,559
13142023-08-238,8236,697,99926,561
13152023-08-247,1796,705,17846,565
13162023-08-256,8726,712,05026,567
13172023-08-266,7306,718,78036,570
13182023-08-276,0126,724,79246,574
13192023-08-281,9056,726,69716,575
13202023-08-298,4856,735,18216,576
13212023-08-307,4706,742,65256,581
13222023-08-318,6836,751,33566,587